Exploring_pippa
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library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.3 ✓ purrr 0.3.4
## ✓ tibble 3.0.5 ✓ dplyr 1.0.3
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.0## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(gt)
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
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## last_plot## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layoutlibrary(patchwork)
sleep<-read.csv("SleepInMammals.csv")sleep## Species.of.animal Body.Weight..kg. Brain.Weight..g.
## 1 African giant pouched rat 1.000 6.60
## 2 Asian elephant 2547.000 4603.00
## 3 Baboon 10.550 179.50
## 4 Big brown bat 0.023 0.30
## 5 Brazilian tapir 160.000 169.00
## 6 Cat 3.300 25.60
## 7 Chimpanzee 52.160 440.00
## 8 Chinchilla 0.425 6.40
## 9 Cow 465.000 423.00
## 10 Eastern American mole 0.075 1.20
## 11 Echidna 3.000 25.00
## 12 European hedgehog 0.785 3.50
## 13 Galago 0.200 5.00
## 14 Goat 27.660 115.00
## 15 Golden hamster 0.120 1.00
## 16 Gray seal 85.000 325.00
## 17 Ground squirrel 0.101 4.00
## 18 Guinea pig 1.040 5.50
## 19 Horse 521.000 655.00
## 20 Lesser short-tailed shrew 0.005 0.14
## 21 Little brown bat 0.010 0.25
## 22 Human 62.000 1320.00
## 23 Mouse 0.023 0.40
## 24 Musk shrew 0.048 0.33
## 25 N. American opossum 1.700 6.30
## 26 Nine-banded armadillo 3.500 10.80
## 27 Owl monkey 0.480 15.50
## 28 Patas monkey 10.000 115.00
## 29 Phanlanger 1.620 11.40
## 30 Pig 192.000 180.00
## 31 Rabbit 2.500 12.10
## 32 Rat 0.280 1.90
## 33 Red fox 4.235 50.40
## 34 Rhesus monkey 6.800 179.00
## 35 Sheep 55.500 175.00
## 36 Tenrec 0.900 2.60
## 37 Tree hyrax 2.000 12.30
## 38 Tree shrew 0.104 2.50
## 39 Vervet 4.190 58.00
## 40 Water opossum 3.500 3.90
## Slow.wave.sleep..hrs.day. Paradoxical.sleep..hrs.day. Total.sleep..hrs.day.
## 1 6.3 2.0 8.3
## 2 2.1 1.8 3.9
## 3 9.1 0.7 9.8
## 4 15.8 3.9 19.7
## 5 5.2 1.0 6.2
## 6 10.9 3.6 14.5
## 7 8.3 1.4 9.7
## 8 11.0 1.5 12.5
## 9 3.2 0.7 3.9
## 10 6.3 2.1 8.4
## 11 8.6 0.0 8.6
## 12 6.6 4.1 10.7
## 13 9.5 1.2 10.7
## 14 3.3 0.5 3.8
## 15 11.0 3.4 14.4
## 16 4.7 1.5 6.2
## 17 10.4 3.4 13.8
## 18 7.4 0.8 8.2
## 19 2.1 0.8 2.9
## 20 7.7 1.4 9.1
## 21 17.9 2.0 19.9
## 22 6.1 1.9 8.0
## 23 11.9 1.3 13.2
## 24 10.8 2.0 12.8
## 25 13.8 5.6 19.4
## 26 14.3 3.1 17.4
## 27 15.2 1.8 17.0
## 28 10.0 0.9 10.9
## 29 11.9 1.8 13.7
## 30 6.5 1.9 8.4
## 31 7.5 0.9 8.4
## 32 10.6 2.6 13.2
## 33 7.4 2.4 9.8
## 34 8.4 1.2 9.6
## 35 3.2 0.6 3.8
## 36 11.0 2.3 13.3
## 37 4.9 0.5 5.4
## 38 13.2 2.6 15.8
## 39 9.7 0.6 10.3
## 40 12.8 6.6 19.4
## Maximum.life.span..years. Gestation.time..days. Predation.index
## 1 4.5 42.0 3
## 2 69.0 624.0 3
## 3 27.0 180.0 4
## 4 19.0 35.0 1
## 5 30.4 392.0 4
## 6 28.0 63.0 1
## 7 50.0 230.0 1
## 8 7.0 112.0 5
## 9 30.0 281.0 5
## 10 3.5 42.0 1
## 11 50.0 28.0 2
## 12 6.0 42.0 2
## 13 10.4 120.0 2
## 14 20.0 148.0 5
## 15 3.9 16.0 3
## 16 41.0 310.0 1
## 17 9.0 28.0 5
## 18 7.6 68.0 5
## 19 46.0 336.0 5
## 20 2.6 21.5 5
## 21 24.0 50.0 1
## 22 100.0 267.0 1
## 23 3.2 19.0 4
## 24 2.0 30.0 4
## 25 5.0 12.0 2
## 26 6.5 120.0 2
## 27 12.0 140.0 2
## 28 20.2 170.0 4
## 29 13.0 17.0 2
## 30 27.0 115.0 4
## 31 18.0 31.0 5
## 32 4.7 21.0 3
## 33 9.8 52.0 1
## 34 29.0 164.0 2
## 35 20.0 151.0 5
## 36 4.5 60.0 2
## 37 7.5 200.0 3
## 38 2.3 46.0 3
## 39 24.0 210.0 4
## 40 3.0 14.0 2
## Sleep.exposure.index Overall.danger.index
## 1 1 3
## 2 5 4
## 3 4 4
## 4 1 1
## 5 5 4
## 6 2 1
## 7 1 1
## 8 4 4
## 9 5 5
## 10 1 1
## 11 2 2
## 12 2 2
## 13 2 2
## 14 5 5
## 15 1 2
## 16 3 1
## 17 1 3
## 18 3 4
## 19 5 5
## 20 2 4
## 21 1 1
## 22 1 1
## 23 1 3
## 24 1 3
## 25 1 1
## 26 1 1
## 27 2 2
## 28 4 4
## 29 1 2
## 30 4 4
## 31 5 5
## 32 1 3
## 33 1 1
## 34 3 2
## 35 5 5
## 36 1 2
## 37 1 3
## 38 2 2
## 39 3 4
## 40 1 1sleep %>%
select(!Gestation.time..days.:Overall.danger.index) %>%
arrange(desc(Body.Weight..kg.)) %>%
gt(rowname_col = "row",
groupname_col = "group") %>%
tab_header(
title = md("**Sleep Patterns in Mammals**")) %>%
tab_footnote(
footnote = "Weight in kilograms",
locations = cells_column_labels(
columns = 2)) %>%
tab_footnote(
footnote = "Weight in grams",
locations = cells_column_labels(
columns = 3)) %>%
tab_footnote(
footnote = "Non-dreaming hours per day",
locations = cells_column_labels(
columns = 4)) %>%
tab_footnote(
footnote = "Dreaming hours per day",
locations = cells_column_labels(
columns = 5)) %>%
cols_label(
Species.of.animal="Species",
Body.Weight..kg.="Body Weight",
Brain.Weight..g. ="Brain Weight",
Slow.wave.sleep..hrs.day. = "Slow Wave Sleep",
Paradoxical.sleep..hrs.day. = "Paradoxical Sleep",
Total.sleep..hrs.day. = "Total Sleep",
Maximum.life.span..years. ="Maximum Life Span (yrs)")| Sleep Patterns in Mammals | ||||||
|---|---|---|---|---|---|---|
| Species | Body Weight1 | Brain Weight2 | Slow Wave Sleep3 | Paradoxical Sleep4 | Total Sleep | Maximum Life Span (yrs) |
| Asian elephant | 2547.000 | 4603.00 | 2.1 | 1.8 | 3.9 | 69.0 |
| Horse | 521.000 | 655.00 | 2.1 | 0.8 | 2.9 | 46.0 |
| Cow | 465.000 | 423.00 | 3.2 | 0.7 | 3.9 | 30.0 |
| Pig | 192.000 | 180.00 | 6.5 | 1.9 | 8.4 | 27.0 |
| Brazilian tapir | 160.000 | 169.00 | 5.2 | 1.0 | 6.2 | 30.4 |
| Gray seal | 85.000 | 325.00 | 4.7 | 1.5 | 6.2 | 41.0 |
| Human | 62.000 | 1320.00 | 6.1 | 1.9 | 8.0 | 100.0 |
| Sheep | 55.500 | 175.00 | 3.2 | 0.6 | 3.8 | 20.0 |
| Chimpanzee | 52.160 | 440.00 | 8.3 | 1.4 | 9.7 | 50.0 |
| Goat | 27.660 | 115.00 | 3.3 | 0.5 | 3.8 | 20.0 |
| Baboon | 10.550 | 179.50 | 9.1 | 0.7 | 9.8 | 27.0 |
| Patas monkey | 10.000 | 115.00 | 10.0 | 0.9 | 10.9 | 20.2 |
| Rhesus monkey | 6.800 | 179.00 | 8.4 | 1.2 | 9.6 | 29.0 |
| Red fox | 4.235 | 50.40 | 7.4 | 2.4 | 9.8 | 9.8 |
| Vervet | 4.190 | 58.00 | 9.7 | 0.6 | 10.3 | 24.0 |
| Nine-banded armadillo | 3.500 | 10.80 | 14.3 | 3.1 | 17.4 | 6.5 |
| Water opossum | 3.500 | 3.90 | 12.8 | 6.6 | 19.4 | 3.0 |
| Cat | 3.300 | 25.60 | 10.9 | 3.6 | 14.5 | 28.0 |
| Echidna | 3.000 | 25.00 | 8.6 | 0.0 | 8.6 | 50.0 |
| Rabbit | 2.500 | 12.10 | 7.5 | 0.9 | 8.4 | 18.0 |
| Tree hyrax | 2.000 | 12.30 | 4.9 | 0.5 | 5.4 | 7.5 |
| N. American opossum | 1.700 | 6.30 | 13.8 | 5.6 | 19.4 | 5.0 |
| Phanlanger | 1.620 | 11.40 | 11.9 | 1.8 | 13.7 | 13.0 |
| Guinea pig | 1.040 | 5.50 | 7.4 | 0.8 | 8.2 | 7.6 |
| African giant pouched rat | 1.000 | 6.60 | 6.3 | 2.0 | 8.3 | 4.5 |
| Tenrec | 0.900 | 2.60 | 11.0 | 2.3 | 13.3 | 4.5 |
| European hedgehog | 0.785 | 3.50 | 6.6 | 4.1 | 10.7 | 6.0 |
| Owl monkey | 0.480 | 15.50 | 15.2 | 1.8 | 17.0 | 12.0 |
| Chinchilla | 0.425 | 6.40 | 11.0 | 1.5 | 12.5 | 7.0 |
| Rat | 0.280 | 1.90 | 10.6 | 2.6 | 13.2 | 4.7 |
| Galago | 0.200 | 5.00 | 9.5 | 1.2 | 10.7 | 10.4 |
| Golden hamster | 0.120 | 1.00 | 11.0 | 3.4 | 14.4 | 3.9 |
| Tree shrew | 0.104 | 2.50 | 13.2 | 2.6 | 15.8 | 2.3 |
| Ground squirrel | 0.101 | 4.00 | 10.4 | 3.4 | 13.8 | 9.0 |
| Eastern American mole | 0.075 | 1.20 | 6.3 | 2.1 | 8.4 | 3.5 |
| Musk shrew | 0.048 | 0.33 | 10.8 | 2.0 | 12.8 | 2.0 |
| Big brown bat | 0.023 | 0.30 | 15.8 | 3.9 | 19.7 | 19.0 |
| Mouse | 0.023 | 0.40 | 11.9 | 1.3 | 13.2 | 3.2 |
| Little brown bat | 0.010 | 0.25 | 17.9 | 2.0 | 19.9 | 24.0 |
| Lesser short-tailed shrew | 0.005 | 0.14 | 7.7 | 1.4 | 9.1 | 2.6 |
|
1
Weight in kilograms
2
Weight in grams
3
Non-dreaming hours per day
4
Dreaming hours per day
|
||||||
sleep_graph<-
sleep %>%
mutate(body_wtlbs = Body.Weight..kg.*2.20462) %>%
arrange(desc(body_wtlbs)) %>%
head(25) %>%
ggplot(aes(
y = body_wtlbs,
x = Total.sleep..hrs.day.,
size = body_wtlbs,
alpha = .5,
color= Species.of.animal))+
geom_point() +
labs(title = "Total hours per day mammals of different weight (lbs) sleep",
x = "",
y = "") +
theme(plot.title.position = "plot",
legend.position = "None")
ggplotly(sleep_graph) %>%
hide_legend()sleep %>%
ggplot(aes(x = Overall.danger.index,
y = fct_reorder(Species.of.animal,
Overall.danger.index,
.desc = FALSE),
fill = Predation.index)) +
geom_col() +
labs(title = "Overall danger index of attacks from other animals for mammals",
x = "",
y = "") +
theme(legend.position = "None",
plot.title.position = "plot")
G0 <-
sleep %>%
mutate(percent_dreaming = (Paradoxical.sleep..hrs.day./Total.sleep..hrs.day.) * 100) %>%
filter(percent_dreaming != 0) %>%
ggplot() +
geom_col(aes(x = percent_dreaming,
y = fct_reorder(Species.of.animal, percent_dreaming, median),
fill = Total.sleep..hrs.day.)) +
labs(title = "How Much do Mammals Dream",
y = "",
x = "Percentage of Hours Dreaming",
fill = "Total Sleeping Hours") +
scale_x_continuous(expand = c(0,0)) +
scale_fill_gradient(high = "green", low = "darkgreen")
G0
G1 <-
sleep %>%
filter(Species.of.animal %in% c("Horse", "Cow", "Pig", "Human", "Sheep", "Chimpanzee", "Goat", "Baboon", "Red fox", "Cat", "Rabbit", "Rat", "Ground squirrel", "Mouse")) %>%
select(Species.of.animal, Slow.wave.sleep..hrs.day.,Sleep.exposure.index) %>%
ggplot() +
geom_col(aes(y = Slow.wave.sleep..hrs.day.,
x = fct_reorder(Species.of.animal,Slow.wave.sleep..hrs.day.,median),
fill = Sleep.exposure.index)) +
geom_text(aes(y = Slow.wave.sleep..hrs.day. + 5, x=fct_reorder(Species.of.animal,Slow.wave.sleep..hrs.day.,median), label= Species.of.animal), color="white", fontface="bold",alpha=0.6, size= 2.5, inherit.aes = FALSE ) +
scale_color_viridis_c(option = "magma") +
labs(title = "Non-Dreaming Hours of Sleep per Day",
caption = "Pippa Gallagher and Joselyn Angeles",
subtitle = "This measures the number of hours of slow wave (non-dreaming) sleep each mammal, on avergae, has each night. The Sleep Measure Index determines how vulnerabel the mammal is when sleeping, with 1 being the least vulnerbale and 5 the most.",
y = "",
x = "",
fill = "Sleep Exposure Index") +
coord_polar(start = 0) +
ylim(-3,18) +
theme(
axis.text.x = element_blank(),
panel.grid = element_blank(),
plot.background = element_rect(fill = "#0a203b"),
panel.background = element_rect(fill = "#0a203b"),
legend.background = element_rect(fill = "#0a203b"),
plot.title = element_text(color = "white"),
legend.text = element_text(color = "white"),
legend.title = element_text(color = "white"),
axis.text.y = element_text(color = "white"),
plot.caption = element_text(color = "white"),
plot.subtitle = element_text(color = "white")) +
scale_fill_gradient(high = "#2473db", low = "#c3deff")
G1
G2 <-
sleep %>%
filter(Species.of.animal %in% c("Horse", "Cow", "Pig", "Human", "Sheep", "Chimpanzee", "Goat", "Baboon", "Red fox", "Cat", "Rabbit", "Rat", "Ground squirrel", "Mouse")) %>%
select(Species.of.animal,Paradoxical.sleep..hrs.day. ,Sleep.exposure.index) %>%
ggplot() +
geom_col(aes(y = Paradoxical.sleep..hrs.day.,
x = fct_reorder(Species.of.animal,Paradoxical.sleep..hrs.day.,median),
fill = Sleep.exposure.index)) +
geom_text(aes(y = Paradoxical.sleep..hrs.day. + 2, x=fct_reorder(Species.of.animal,Paradoxical.sleep..hrs.day.,median), label= Species.of.animal), color="white", fontface="bold",alpha=0.6, size=2.5, inherit.aes = FALSE ) +
scale_color_viridis_c(option = "magma") +
labs(title = "Dreaming Hours of Sleep per Day",
caption = "Pippa Gallagher and Joselyn Angeles",
subtitle = "This measures the number of hours of paradoxical (dreaming) sleep each mammal, on avergae, has each night. The Sleep Measure Index determines how vulnerabel the mammal is when sleeping, with 1 being the least vulnerbale and 5 the most.",
y = "",
x = "",
fill = "Sleep Exposure Index") +
coord_polar(start = 0) +
ylim(-1.5,6) +
theme(
axis.text.x = element_blank(),
panel.grid = element_blank(),
plot.background = element_rect(fill = "#0a203b"),
panel.background = element_rect(fill = "#0a203b"),
legend.background = element_rect(fill = "#0a203b"),
plot.title = element_text(color = "white"),
legend.text = element_text(color = "white"),
legend.title = element_text(color = "white"),
axis.text.y = element_text(color = "white"),
plot.caption = element_text(color = "white"),
plot.subtitle = element_text(color = "white")) +
scale_fill_gradient(high = "#2473db", low = "#c3deff")
G2
sleep %>%
ggplot() +
geom_violin(aes(x = Overall.danger.index,
y = Maximum.life.span..years.,
color = Overall.danger.index)) +
theme(
plot.background = element_rect("black"),
panel.background = element_rect("black"),
plot.title = element_text(color = "lightgrey"),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
legend.position = "none"
) +
labs(x = "",
y = "",
title = "Maximum Life Span and Danger Index")